How to hire top data scientists
Data science is emerging as an important field, having one of the most sought after positions in the technology as well as the business world. However, this demand is quickly outstripping its supply. Today, the ambiguity surrounding the field and its potential lack of professional expertise in companies is making it extremely difficult for such companies which are seeking to hire a data scientist or team of data scientists for the first time or even to build a competent team of analytics.
However, with a brief understanding of data scientist types, we can provide recruiters with a more tailored understanding of the process of hiring a data scientist.
In general, data scientists have skills pertaining to either one, two or three main areas. They are:
- Mathematics, Statistics and Machine Learning.
- Coding and Software Engineering
- Overall expertise in particular industries.
Experienced data scientists possess strong skill sets in either two or three of the key areas mentioned above. For example, a data scientist expert in software and math are best suited for tech companies or production roles whereas someone yielding expertise in software and math are more suited to scientific research. Someone possessing a strong skill in all the three areas are called ‘unicorns’. They generally tend to be late career folks or consultant by the time they develop expertise in all the three areas. In addition to these skills, a solid data science candidate also should possess the knowledge of scientific research processes and solid communication skills to change results into successful business solutions.
Listed below are five strategies to help improve your chances of hiring the best data scientist in the market
- Ensure the hiring process is an all year round and continuously improvised:
- Although it is common to assume hiring as a task one needs to occasionally participate in, however, if you are always on the market for recruiting then whenever any great talent comes along, you’ll have the opportunity to engage with them.
- Moreover, when you are always in a process of hiring, it provides you with experience and understanding about the field. This experience can be used to bring consistency in protocol results and increase your success rate at recruiting ultimately forcing you to treat your talent channel with the same care you manage your data pipelines.
- Ensure your hiring process reflects your hiring needs:
- The standard interviewing questions are flawed. They evaluate a candidate’s prior experience and their ability to parrot knowledge all of which is unnecessary for a data scientist when in practice. To tackle these flaws one must first have a clear understanding of how they want their data scientist to perform and produce.
- An amazing way to implement this in your hiring process is by first identifying five problems or tasks you would really like to see a data scientist tackle. For each such task ensure that you have or could reasonably gather the data required for it and envision an effective solution yourself. This puts the candidates into an environment that will closely resemble what their ‘day-to-day’ environment at work would be like and it will make it easier for you to identify ideal candidates under such circumstances.
- Minimize your biases before evaluating candidates:
- It is human nature to form opinions (conscious or unconscious) about individuals as soon as one enters the room. Thus, a wide range of biases always find their way into the process of hiring and the most common biases under this category is our general tendency to prefer candidates who are similar to ourselves.
- However, the most efficient way to rid your hiring process from biases such as the one mentioned above, is by designing a process to test a candidate’s skills first and then progress to their problem solving capability and communication strength. Only then can you evaluate how the candidate will perform and fit into the culture, thereby forming your own subjective opinion about them.
- Conceptualize your hiring process in a way that sells to the candidates:
- Most interviews today tend to be mundane and tedious, always following the same question pattern and evaluation tests. At a time like this, it becomes very important to design the hiring process in a way where you are able to provide to the candidates real data and problems reflecting real challenges they might face at your company once they are employed.
- This ensures that the candidates engaging in your hiring process will get a glimpse of your team’s dynamic work culture and understand what it will be like to work in your company. This generates a trusting sense in the participants that will also help promote the goodwill of your company.
- Make decisions together with your team:
- Every manager has to make difficult decisions. Thus a clear way to make your objectives clear during the hiring process is by establishing a clear framework for evaluating the candidates at every step of the process which everyone in your team can collectively agree to.
- But most importantly, it is crucial to make decisions openly as a team. This makes sure that the hiring manager can gather direct feedback from everyone involved in the process about a particular candidate.
Hopefully these insights and tips help you build a great data science team and enable you to put your best foot forward and stack the deck in your favor.